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A novel approach to automated behavioral diagram assessment using label similarity and subgraph edit distance

Fauzan Reza, Siahaan Daniel Oranova, Rochimah Siti, Triandini Evi

Computer Science

2021

T. 22 (2)

191–207

The Unified Modeling Language (UML) is one of the standard languages that are used in modeling software; therefore, UML is widely taught in many universities. Generally, teachers assign students to build UML diagram designs based on a predetermined project; however, the assessment of such assignments can be challenging, and teachers may be inconsistent in assessing their students’ answers. Thus, automated UML diagram assessment becomes essential to maintaining assessment consistency. This study uses a behavioral diagram as the object of research, since it is a commonly taught UML diagram. The behavioral diagram can show a dynamic view of the software. This study proposes a new approach to automatically assessing the similarity of behavior diagrams as reliably as experts do. We divide the assessment into two portions: semantic assessment, and structural assessment. Label similarity is used to calculate semantic assessment, while subgraph edit distance is used to calculate structural assessment. The results suggest that the proposed approach is as reliable as an expert in assessing the similarity between two behavior diagrams. The observed agreement value suggests a strong agreement between the use of experts and the proposed approach.